"Optical Character Recognition (OCR) Technology" matching MCP servers:

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

MCP server for intelligent circuit diagram recognition, enabling detection of components, text, topology, and netlist from circuit images.

MCP server that converts file contents into compact, line-numbered PNG images for vision models to read, reducing token usage by roughly 7x for large files.

Exposes ComfyUI-generated character avatars as tools, allowing control of expressions and poses via preset workflows with adjustable parameters.

Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.

Enables OCR-based text, table, handwriting, and formula recognition from images via MCP, supporting local files and URLs for integration with AI assistants.

Provides advanced OCR capabilities with multiple state-of-the-art backends (DeepSeek-OCR, Florence-2, DOTS.OCR, PP-OCRv5), supporting document processing, scanner integration, and multi-format output with layout preservation.

Converts screenshots of web pages into structured Markdown using GLM-OCR (Zhipu), with tools for OCR on images, webpages, and health checks.

Enables AI assistants and applications to perform optical character recognition (OCR) from images via stdin/stdout streams or REST API endpoints.

An MCP server that provides local image recognition on macOS, including OCR, image classification, comprehensive image analysis, and screenshot recognition, all via Apple's Vision framework without any network requests.

Enables MCP clients to perform OCR on images via multimodal models through a remote server.

Enables integration between MCP clients and the Handwriting OCR service, allowing users to upload images and PDF documents, check processing status, and retrieve OCR results as Markdown.

Provides image recognition capabilities using Anthropic Claude Vision and OpenAI GPT-4 Vision APIs, supporting multiple image formats and offering optional text extraction via Tesseract OCR.

A Model Context Protocol server that gives AI assistants OCR with first-class accuracy handling and evaluation. It wraps three engines behind one interface and can score and compare them.

OpenAI-compatible vision recognition MCP server that lets MCP clients see images by routing them to any OpenAI-compatible vision model.

Enables AI agents to extract text from Brazilian court case PDFs and images fully offline, checking legibility, OCR-ing only the pages with corrupted fonts, and returning page-level text labelled as native text or OCR with sheet numbers ready for citation. Supports single pages, whole files, and batch folders of up to 50 PDFs per call.

Gives vision-less LLMs the ability to recognize clipboard screenshots and images by proxying to an OpenAI-compatible vision model.

Enables users to create character spritesheets from natural-language requests, preview animations, search clothing and tool items, and export engine-ready assets for Godot, Unity, and web.

Enables MCP clients to run OCR on local image files through a single long-lived PaddleOCR backend shared across clients, returning recognized lines as a Markdown table with optional coordinate and confidence columns.

A lightweight server that provides detailed text analysis, counting total characters, characters without spaces, letters, numbers, and symbols for AI assistants like Claude Desktop and GitHub Copilot.